Bulletin of the American Physical Society
APS April Meeting 2019
Volume 64, Number 3
Saturday–Tuesday, April 13–16, 2019; Denver, Colorado
Session L10: Tonne Scale Neutrinoless Double Beta Decay R&D V
3:30 PM–5:18 PM,
Sunday, April 14, 2019
Sheraton
Room: Governor's Square 12
Sponsoring
Units:
DNP DPF
Chair: Reina Maruyama, Yale University
Abstract: L10.00008 : Signal-background Discrimination in EXO-200 with Deep Learning*
4:54 PM–5:06 PM
View Presentation Abstract
Presenter:
Thomas H Richards
(University of Alabama)
Author:
Thomas H Richards
(University of Alabama)
Collaboration:
EXO-200
We develop a deep neural network (DNN) algorithm to discriminate signal and background events in the EXO-200 neutrinoless double beta decay experiment. In the studied case, the DNN is able to classify Monte Carlo events as signal (neutrinoless double beta decay of 136Xe) or different backgrounds directly from the waveforms. The classification accuracy exceeds that of conventional algorithms used in the experiment, trained over the same data. The accuracy of background classification is checked with real detector calibration data for the most important background types (gamma decays of 238U/232Th decay chains and 60Co, two-neutrino double beta decay of 136Xe, beta decay of 137Xe). Additional checks to substantiate the validity of the network’s performance will be presented.
*Department of Energy Grant No. DE-SC0019261
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